PPubMed2 Jun 2022

Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Yijun Li, Stefan Stanojevic, Lana X. Garmire

Abstract

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

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The review surveys emerging AI techniques for spatial transcriptomics, categorizing methods, summarizing performance, and highlighting challenges.

What’s new

i.Comprehensive up-to-date survey of AI methods for spatial transcriptomics analysis
ii.Classification of methods by application areas such as SVG detection, clustering, deconvolution, and enhancement
iii.Compilation of method details, code links, and reported limitations in summary tables

How they did it

The authors performed a literature review, collecting published AI approaches for spatial transcriptomics up to 2022. They organized methods by computational task, extracted key features, performance notes, and software availability, and presented them in tables and descriptive text.

What they found

SOMDE is reported to be 5–50 times more computationally efficient than spatialDE, SPARK, Giotto, and scGCO
scGCO detected significantly more spatially variable genes than spatialDE while using less memory on mouse olfactory bulb data

Caveats

✕Reproducibility of spatially variable gene detection for many methods remains unvalidated
✕Performance of image‑based methods depends on availability and resolution of histology images
✕Some deconvolution tools assume matched reference datasets or have limited robustness across tissue types
✕Parameter tuning for contrastive learning frameworks (e.g., conST) is non‑trivial

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